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# tests/test_models/test_positional_encoding.py
"""
Tests for positional encoding.
"""
import matplotlib
import torch
matplotlib.use("Agg") # use non-interactive backend for test environments
from src.models.positional_encoding import PositionalEncoding
class TestPositionalEncoding:
"""Test suite for PositionalEncoding."""
def test_output_shape(self):
"""Test that output shape matches input shape."""
d_model, max_len = 512, 5000
batch_size, seq_len = 2, 100
pos_enc = PositionalEncoding(d_model, max_len, dropout=0.0)
x = torch.randn(batch_size, seq_len, d_model)
output = pos_enc(x)
assert output.shape == (batch_size, seq_len, d_model)
def test_different_sequence_lengths(self):
"""Test with various sequence lengths."""
pos_enc = PositionalEncoding(d_model=256, max_len=1000, dropout=0.0)
for seq_len in [10, 50, 100, 500]:
x = torch.randn(1, seq_len, 256)
output = pos_enc(x)
assert output.shape == (1, seq_len, 256)
def test_dropout_changes_output(self):
"""Test that dropout is applied during training."""
torch.manual_seed(42)
pos_enc = PositionalEncoding(d_model=128, dropout=0.5)
pos_enc.train()
x = torch.randn(2, 10, 128)
output1 = pos_enc(x)
output2 = pos_enc(x)
# Should be different due to dropout
assert not torch.allclose(output1, output2)
# In eval mode, should be deterministic
pos_enc.eval()
output3 = pos_enc(x)
output4 = pos_enc(x)
assert torch.allclose(output3, output4)
def test_encoding_properties(self):
"""Test mathematical properties of encoding."""
pos_enc = PositionalEncoding(d_model=128, max_len=100, dropout=0.0)
# Get the raw encoding (without dropout)
pe = pos_enc.pe[0] # Remove batch dimension
# Each row should have values in [-1, 1] (sin/cos range)
assert (pe >= -1).all() and (pe <= 1).all()
# Different positions should have different encodings
assert not torch.allclose(pe[0], pe[1])
assert not torch.allclose(pe[0], pe[50])
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